import numpy as np
import pandas as pd
import geopandas as gpd
import fiona
import glob
import os
import contextily as ctx
from scipy.spatial import cKDTree
from shapely.geometry import Point
import json
from tqdm.auto import tqdm
pd.set_option('min_rows', 30)
import sys
sys.path.append('..')
from importlib import reload
# import src.utils as utils
# reload(utils)
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (16, 16)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 10)

Setup

%%time
parcels = gpd.read_file('input/lds-nz-primary-parcels-CLIPPED-4326.gpkg')

parcels_sample = parcels.sample(10000)

# keep a centroid and polygon version - change between them according to need
parcels_sample['geometry_polygon_2193'] = parcels_sample.geometry.to_crs(2193)
parcels_sample['geometry_centroid_2193'] = parcels_sample.geometry_polygon_2193.centroid
parcels_sample['geometry_centroid_4326'] = parcels_sample.geometry_centroid_2193.to_crs(4326)
parcels_sample['geometry_polygon_4326'] = parcels_sample.geometry_polygon_2193.to_crs(4326)

1. LINZ parcel information

a. LINZ_parcel_ID Parcel identifier - “certificate of title”.

parcels_sample['LINZ_parcel_ID'] = parcels_sample.id

b. LINZ_parcel_centroid_lon

c. LINZ_parcel_centroid_lat

parcels_sample['geometry'] = parcels_sample.geometry_centroid_4326
parcels_sample['LINZ_parcel_centroid_lon'] = parcels_sample.geometry.x
parcels_sample['LINZ_parcel_centroid_lat'] = parcels_sample.geometry.y

d. LINZ_parcel_vertices_lon vector of longitudes of the vertices of the matched LINZ parcels

e. LINZ_parcel_vertices_lat

parcels_sample['geometry'] = parcels_sample.geometry_polygon_4326
# all are multipolygons
print(set([type(a) for a in parcels_sample.geometry]))

{<class 'shapely.geometry.multipolygon.MultiPolygon'>}

coordinates_all = []
for multipolygon in tqdm(parcels_sample.geometry):
    coordinates = []
    for polygon in multipolygon:
        coordinates.extend(polygon.exterior.coords)
    coordinates_all.append(coordinates)

parcels_sample['LINZ_parcel_vertices_lon'] = [[p[0] for p in points] for points in coordinates_all]
parcels_sample['LINZ_parcel_vertices_lat'] = [[p[1] for p in points] for points in coordinates_all]

f. LINZ_parcel_roadvertices_lon subvector of longitudes of parcel that sits adjacent to a road

g. LINZ_parcel_roadvertices_lat

h. LINZ_parcel_sides_zones AUP Zone Code of adjoining parcels (this includes residential, business, and rural zones; it should also include roads, water and open spaces)

i. i. LINZ_TRNSPWR_ohead_indicator Indicator (0 or 1) for LINZ parcel located under overhead transpower line. Leave blank otherwise. Note that ‘TRANSLINE’ denotes overhead transmission lines in the GIS dataset, while ‘CABLE’ denotes underground transmission lines and can be ignored.

i. ii. LINZ_TRNSPWR_ohead_name

j. i. LINZ_VWSHFT_ohead_indicator Indicator (0 or 1) for LINZ parcel located under viewshafts. Leave blank otherwise.

j. ii. LINZ_VWSHFT_ohead_name Name of the volcanic cone (e.g. Mt Albert). Leave blank if no viewshaft applies.

j. iii. LINZ_VWSHFT_ohead_ID OBJECTID of the viewshaft. Leave blank if no viewshaft applies.

2. AUP shapefile information. Calculations are based on centroid of matched parcels:

a. LINZmatch_AUP_name AUP zone (string) consent is located in (from the AUP shapefiles)

b. LINZmatch_AUP_code AUP Zone Code (numerical) consent is located in

%%time
aup_zones = gpd.read_file('restricted/2016_Unitary_Plan_operational_15Nov/UP_OIP_15Nov2016_SHP/MASTER_UP_BaseZone.shp')
aup_zones = aup_zones.to_crs(2193)
aup_zones.sample(3)

CPU times: user 31.2 s, sys: 2.21 s, total: 33.4 s
Wall time: 33.3 s

	WORKFLOWJO	last_edi00	CONTOUR	created_da	DocumentUR	GlobalID	GROUPZONE	GROUPZONE_	ID	NAME	OBJECTID	PARCEL_BAS	PARCEL_B00	PRECINCT	PRECINCT_r	PRECINCTGR	PRECINCT00	SCHEDULE	SHAPE_Area	SHAPE_Leng	STATUS	SUBPRECINC	SUBPRECI00	SUBTYPE	SUBTYPE_re	TYPE	TYPE_resol	VALIDATION	VALIDATI00	VERSIONSTA	VERSIONS00	ZONE	ZONE_resol	ZONEHEIGHT	geometry
58724	NaN	20161111011	None	20160718211	None	{0CCC66B0-9A15-477A-BE4F-FAE9C5FA9C15}	5	Coastal	None	None	58725	None	None	None	None	None	None	None	62.153950	58.346375	None	None	None	None	None	None	None	3	Valid and Public	4	Operative	59	Coastal - Coastal Transition Zone	NaN	POLYGON Z ((1756089.130 5973206.276 0.000, 175...
103279	NaN	20161111012	None	20160718211	None	{111B7716-6401-457F-A260-9BA18D7E0DB9}	5	Coastal	None	None	103280	None	None	None	None	None	None	None	372.662968	106.669373	None	None	None	None	None	None	None	3	Valid and Public	4	Operative	30	Coastal - General Coastal Marine Zone	NaN	POLYGON Z ((1810661.036 5993908.378 0.000, 181...
54081	NaN	20161111011	None	20160718211	None	{CD16D435-B3A5-4C26-BFC7-E6AF32E60231}	7	General	None	None	54082	None	None	None	None	None	None	None	41502.960080	1179.183755	None	None	None	None	None	None	None	3	Valid and Public	4	Operative	26	Strategic Transport Corridor Zone	NaN	POLYGON Z ((1737071.120 5973948.034 0.000, 173...

%%time
parcels_sample['geometry'] = parcels_sample['geometry_centroid_2193']
parcels_sample = gpd.sjoin(parcels_sample, aup_zones[['ZONE', 'ZONE_resol', 'geometry']]).drop(columns=['index_right'])
parcels_sample = parcels_sample.rename(columns={'ZONE': 'LINZmatch_AUP_code', 'ZONE_resol': 'LINZmatch_AUP_name'})

CPU times: user 59.9 s, sys: 0 ns, total: 59.9 s
Wall time: 1min

ax = parcels_sample[parcels_sample.LINZmatch_AUP_name.str.contains('Business')].sample(500).plot(alpha=0.5)
ctx.add_basemap(ax, crs=2193)

c. Hdist_rural Minimum Haversine distance to nearest Rural Zone, plus

c. i. Hdist_rural_name Name of rural zone identified

c. ii. Hdist_rural_code Numeric code of rural zone identified

I've included all of these as rural:
['Rural - Mixed Rural Zone',
'Rural - Rural Coastal Zone',
'Rural - Countryside Living Zone',
'Rural - Rural Production Zone',
'Rural - Rural Conservation Zone',
'Rural - Waitakere Ranges Zone',
'Rural - Waitakere Foothills Zone']

rural_codes = aup_zones[aup_zones.ZONE_resol.str.lower().str.contains('rural - ', na=False)]['ZONE'].unique()

# check that each rural zone code matches with a unique rural zone name 
assert all([len(aup_zones[aup_zones.ZONE == code].ZONE_resol.unique()) == 1 for code in rural_codes])
# dictionary mapping code to names
rural_code2name = {code: aup_zones[aup_zones.ZONE == code].ZONE_resol.unique()[0] for code in rural_codes}

aup_zones[aup_zones.ZONE_resol.isna()]

	WORKFLOWJO	last_edi00	CONTOUR	created_da	DocumentUR	GlobalID	GROUPZONE	GROUPZONE_	ID	NAME	OBJECTID	PARCEL_BAS	PARCEL_B00	PRECINCT	PRECINCT_r	PRECINCTGR	PRECINCT00	SCHEDULE	SHAPE_Area	SHAPE_Leng	STATUS	SUBPRECINC	SUBPRECI00	SUBTYPE	SUBTYPE_re	TYPE	TYPE_resol	VALIDATION	VALIDATI00	VERSIONSTA	VERSIONS00	ZONE	ZONE_resol	ZONEHEIGHT	geometry
20853	NaN	20161111011	None	20160718211	None	{2D83F680-9587-4D57-AF0B-CB7CA27C3D2C}	6	Special purpose zone	None	None	20854	None	None	None	None	None	None	None	22.286945	72.629617	None	None	None	None	None	None	None	3	Valid and Public	4	Operative	58	None	NaN	POLYGON Z ((1767684.860 5903714.023 33.721, 17...
121765	NaN	20161111010	None	20160718211	None	{FD3E8BB4-1979-42B5-9A77-5D04AE5190AF}	6	Special purpose zone	None	None	121766	None	None	None	None	None	None	None	4.114962	86.135341	None	None	None	None	None	None	None	3	Valid and Public	4	Operative	58	None	NaN	POLYGON Z ((1767684.083 5903713.690 33.721, 17...

# 2 NAs in ZONE_resol are from a zone 58, which only has observations
aup_zones[aup_zones.ZONE == '58']

	WORKFLOWJO	last_edi00	CONTOUR	created_da	DocumentUR	GlobalID	GROUPZONE	GROUPZONE_	ID	NAME	OBJECTID	PARCEL_BAS	PARCEL_B00	PRECINCT	PRECINCT_r	PRECINCTGR	PRECINCT00	SCHEDULE	SHAPE_Area	SHAPE_Leng	STATUS	SUBPRECINC	SUBPRECI00	SUBTYPE	SUBTYPE_re	TYPE	TYPE_resol	VALIDATION	VALIDATI00	VERSIONSTA	VERSIONS00	ZONE	ZONE_resol	ZONEHEIGHT	geometry
20853	NaN	20161111011	None	20160718211	None	{2D83F680-9587-4D57-AF0B-CB7CA27C3D2C}	6	Special purpose zone	None	None	20854	None	None	None	None	None	None	None	22.286945	72.629617	None	None	None	None	None	None	None	3	Valid and Public	4	Operative	58	None	NaN	POLYGON Z ((1767684.860 5903714.023 33.721, 17...
121765	NaN	20161111010	None	20160718211	None	{FD3E8BB4-1979-42B5-9A77-5D04AE5190AF}	6	Special purpose zone	None	None	121766	None	None	None	None	None	None	None	4.114962	86.135341	None	None	None	None	None	None	None	3	Valid and Public	4	Operative	58	None	NaN	POLYGON Z ((1767684.083 5903713.690 33.721, 17...

rural = aup_zones[aup_zones.ZONE.isin(rural_codes)]

%%time
# method 1, slightly slower: dissolve by zone
rural_by_zone = rural.dissolve(by='ZONE').reset_index(drop=False)
distances = []
codes = []
for index, row in tqdm(parcels_sample.iterrows(), total=len(parcels_sample)):
    distance_candidates = []
    code_candidates = []
    for index, rural_row in rural_by_zone.iterrows():
        distance_candidates.append(row.geometry.distance(rural_row.geometry))
        code_candidates.append(rural_row.ZONE)
    distances.append(distance_candidates)
    codes.append(code_candidates)
    
# all distances (to any zone)
distances = np.array(distances)
codes = np.array(codes)
# indices of minimum distances
min_idx = np.argmin(distances, axis=-1)

CPU times: user 1min 2s, sys: 363 ms, total: 1min 3s
Wall time: 1min 2s

%%time
# method 2, slightly faster: dissolve all after subsetting gpd dfs
rural_by_zone_dict = {code: rural[rural.ZONE == code].dissolve() for code in rural_codes}

distances = []
codes = []
for index, row in tqdm(parcels_sample.iterrows(), total=len(parcels_sample)):
    distance_candidates = []
    code_candidates = []
    for code, rural_gdf in rural_by_zone_dict.items():
        distance_candidates.append(row.geometry.distance(rural_gdf.geometry[0]))
        code_candidates.append(rural_gdf.ZONE[0])
    distances.append(distance_candidates)
    codes.append(code_candidates)

# all distances (to any zone)
distances = np.array(distances)
codes = np.array(codes)
# indices of minimum distances
min_idx = np.argmin(distances, axis=-1)

CPU times: user 56.2 s, sys: 289 ms, total: 56.5 s
Wall time: 56.2 s

parcels_sample['Hdist_rural'] = distances[(np.arange(len(distances)), min_idx)]
parcels_sample['Hdist_rural_code'] = codes[(np.arange(len(distances)), min_idx)]
parcels_sample['Hdist_rural_name'] = parcels_sample.apply(lambda x: rural_code2name[x.Hdist_rural_code], axis=1)

import matplotlib.patches as mpatches
import matplotlib.pyplot as plt

colours = ('blue', 'orange', 'green', 'red', 'purple', 'brown', 'pink', 'cyan')
name2colour = {name: colour for name, colour in zip(rural_code2name.values(), colours)}

column = 'Hdist_rural'
subsample = parcels_sample[parcels_sample[column] > 0].sample(5)
subsample['buffer'] = subsample.apply(lambda x: x.geometry.buffer(x[column]), axis=1)
subsample['geometry'] = subsample['buffer']
subsample = subsample[~subsample.is_empty]
# ax = rural.plot(column='ZONE_resol', legend=True)
# subsample.plot(column='Hdist_rural_name', alpha=0.4, ax=ax)
ax = rural.plot(color=[name2colour[z] for z in rural.ZONE_resol])
subsample.plot(color=[name2colour[z] for z in subsample.Hdist_rural_name], alpha=0.65, ax=ax)
plt.ylim((5.88e6, 5.96e6))

# add legend
legend_patches = [mpatches.Patch(color=col, label=name) for name, col in name2colour.items()]
plt.legend(handles=legend_patches)

ctx.add_basemap(ax=ax, crs=2193)

import matplotlib
matplotlib.colors

<module 'matplotlib.colors' from '/data/miniconda3/envs/house-upzone/lib/python3.8/site-packages/matplotlib/colors.py'>

d. Hdist_bus Minimum Haversine distance to nearest Business Zone

d. i. Hdist_bus_name

d. ii. Hdist_bus_code

business_codes = aup_zones[aup_zones.ZONE_resol.str.lower().str.contains('business - ', na=False)]['ZONE'].unique()

# check that each business zone code matches with a unique business zone name 
assert all([len(aup_zones[aup_zones.ZONE == code].ZONE_resol.unique()) == 1 for code in business_codes])
# dictionary mapping code to names
business_code2name = {code: aup_zones[aup_zones.ZONE == code].ZONE_resol.unique()[0] for code in business_codes}
business_code2name

{'44': 'Business - Neighbourhood Centre Zone',
 '12': 'Business - Mixed Use Zone',
 '17': 'Business - Light Industry Zone',
 '5': 'Business - Heavy Industry Zone',
 '49': 'Business - General Business Zone',
 '1': 'Business - Business Park Zone',
 '22': 'Business - Town Centre Zone',
 '10': 'Business - Metropolitan Centre Zone',
 '7': 'Business - Local Centre Zone',
 '35': 'Business - City Centre Zone'}

business = aup_zones[aup_zones.ZONE.isin(business_codes)]

%%time
# method 1, slightly slower: dissolve by zone
business_by_zone = business.dissolve(by='ZONE').reset_index(drop=False)
distances = []
codes = []
for index, row in tqdm(parcels_sample.iterrows(), total=len(parcels_sample)):
    distance_candidates = []
    code_candidates = []
    for index, business_row in business_by_zone.iterrows():
        distance_candidates.append(row.geometry.distance(business_row.geometry))
        code_candidates.append(business_row.ZONE)
    distances.append(distance_candidates)
    codes.append(code_candidates)
    
# all distances (to any zone)
distances = np.array(distances)
codes = np.array(codes)
# indices of minimum distances
min_idx = np.argmin(distances, axis=-1)

CPU times: user 1min 2s, sys: 363 ms, total: 1min 3s
Wall time: 1min 2s

%%time
# method 2, slightly faster: dissolve all after subsetting gpd dfs
business_by_zone_dict = {code: business[business.ZONE == code].dissolve() for code in business_codes}

distances = []
codes = []
for index, row in tqdm(parcels_sample.iterrows(), total=len(parcels_sample)):
    distance_candidates = []
    code_candidates = []
    for code, business_gdf in business_by_zone_dict.items():
        distance_candidates.append(row.geometry.distance(business_gdf.geometry[0]))
        code_candidates.append(business_gdf.ZONE[0])
    distances.append(distance_candidates)
    codes.append(code_candidates)

# all distances (to any zone)
distances = np.array(distances)
codes = np.array(codes)
# indices of minimum distances
min_idx = np.argmin(distances, axis=-1)

CPU times: user 44.9 s, sys: 230 ms, total: 45.1 s
Wall time: 44.8 s

parcels_sample['Hdist_bus'] = distances[(np.arange(len(distances)), min_idx)]
parcels_sample['Hdist_bus_code'] = codes[(np.arange(len(distances)), min_idx)]
parcels_sample['Hdist_bus_name'] = parcels_sample.apply(lambda x: business_code2name[x.Hdist_bus_code], axis=1)

import matplotlib.patches as mpatches
import matplotlib.pyplot as plt

colours = ('blue', 'orange', 'green', 'red', 'purple', 'brown', 'pink', 'cyan', 'black', 'white')
name2colour = {name: colour for name, colour in zip(business_code2name.values(), colours)}

# hard to see, subset to smaller area
bounds = {'x1': 1.76e6, 'x2': 1.7613e6, 'y1': 5.9123e6, 'y2': 5.914e6}

column = 'Hdist_bus'
subsample = parcels_sample.cx[bounds['x1']:bounds['x2'], bounds['y1']:bounds['y2']]
subsample['buffer'] = subsample.apply(lambda x: x.geometry.buffer(x[column]), axis=1)
subsample['geometry'] = subsample['buffer']

subsample = subsample[~subsample.is_empty]

business_plot = business.cx[bounds['x1']:bounds['x2'], bounds['y1']:bounds['y2']]
ax = business_plot.plot(color=[name2colour[z] for z in business_plot.ZONE_resol])
subsample.plot(color=[name2colour[z] for z in subsample.Hdist_bus_name], alpha=0.65, ax=ax)
# plt.ylim((5.88e6, 5.96e6))

# add legend
legend_patches = [mpatches.Patch(color=col, label=name) for name, col in name2colour.items()]
plt.legend(handles=legend_patches)

ctx.add_basemap(ax=ax, crs=2193)

/data/miniconda3/envs/house-upzone/lib/python3.8/site-packages/geopandas/geodataframe.py:1322: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super(GeoDataFrame, self).__setitem__(key, value)

e. Hdist_resid Minimum Haversine distance to nearest Residential Zone,

e. i. Hdist_resid_name

e. ii. Hdist_resid_code

resid_codes = aup_zones[aup_zones.ZONE_resol.str.lower().str.contains('resid', na=False)]['ZONE'].unique()

resid_codes

array(['60', '23', '18', '8', '19', '20'], dtype=object)

# check that each resid zone code matches with a unique resid zone name 
assert all([len(aup_zones[aup_zones.ZONE == code].ZONE_resol.unique()) == 1 for code in resid_codes])
# dictionary mapping code to names
resid_code2name = {code: aup_zones[aup_zones.ZONE == code].ZONE_resol.unique()[0] for code in resid_codes}
resid_code2name

{'60': 'Residential - Mixed Housing Urban Zone',
 '23': 'Residential - Large Lot Zone',
 '18': 'Residential - Mixed Housing Suburban Zone',
 '8': 'Residential - Terrace Housing and Apartment Building Zone',
 '19': 'Residential - Single House Zone',
 '20': 'Residential - Rural and Coastal Settlement Zone'}

resid = aup_zones[aup_zones.ZONE.isin(resid_codes)]

%%time
# method 1, slightly slower: dissolve by zone
resid_by_zone = resid.dissolve(by='ZONE').reset_index(drop=False)
distances = []
codes = []
for index, row in tqdm(parcels_sample.iterrows(), total=len(parcels_sample)):
    distance_candidates = []
    code_candidates = []
    for index, resid_row in resid_by_zone.iterrows():
        distance_candidates.append(row.geometry.distance(resid_row.geometry))
        code_candidates.append(resid_row.ZONE)
    distances.append(distance_candidates)
    codes.append(code_candidates)
    
# all distances (to any zone)
distances = np.array(distances)
codes = np.array(codes)
# indices of minimum distances
min_idx = np.argmin(distances, axis=-1)

CPU times: user 1min 2s, sys: 363 ms, total: 1min 3s
Wall time: 1min 2s

%%time
# method 2, slightly faster: dissolve all after subsetting gpd dfs
resid_by_zone_dict = {code: resid[resid.ZONE == code].dissolve() for code in resid_codes}

distances = []
codes = []
for index, row in tqdm(parcels_sample.iterrows(), total=len(parcels_sample)):
    distance_candidates = []
    code_candidates = []
    for code, resid_gdf in resid_by_zone_dict.items():
        distance_candidates.append(row.geometry.distance(resid_gdf.geometry[0]))
        code_candidates.append(resid_gdf.ZONE[0])
    distances.append(distance_candidates)
    codes.append(code_candidates)

# all distances (to any zone)
distances = np.array(distances)
codes = np.array(codes)
# indices of minimum distances
min_idx = np.argmin(distances, axis=-1)

CPU times: user 2min 9s, sys: 509 ms, total: 2min 9s
Wall time: 2min 8s

parcels_sample['Hdist_resid'] = distances[(np.arange(len(distances)), min_idx)]
parcels_sample['Hdist_resid_code'] = codes[(np.arange(len(distances)), min_idx)]
parcels_sample['Hdist_resid_name'] = parcels_sample.apply(lambda x: resid_code2name[x.Hdist_resid_code], axis=1)

import matplotlib.patches as mpatches
import matplotlib.pyplot as plt

colours = ('blue', 'orange', 'green', 'red', 'purple', 'brown', 'pink', 'cyan', 'black', 'white')
name2colour = {name: colour for name, colour in zip(resid_code2name.values(), colours)}

# hard to see, subset to smaller area
bounds = {'x1': 1.76e6, 'x2': 1.7613e6, 'y1': 5.9123e6, 'y2': 5.914e6}

column = 'Hdist_resid'
subsample = parcels_sample.cx[bounds['x1']:bounds['x2'], bounds['y1']:bounds['y2']]
subsample['buffer'] = subsample.apply(lambda x: x.geometry.buffer(x[column]), axis=1)
subsample['geometry'] = subsample['buffer']

subsample = subsample[~subsample.is_empty]

resid_plot = resid.cx[bounds['x1']:bounds['x2'], bounds['y1']:bounds['y2']]
ax = resid_plot.plot(color=[name2colour[z] for z in resid_plot.ZONE_resol])
subsample.plot(color=[name2colour[z] for z in subsample.Hdist_resid_name], alpha=0.65, ax=ax)
# plt.ylim((5.88e6, 5.96e6))

# add legend
legend_patches = [mpatches.Patch(color=col, label=name) for name, col in name2colour.items()]
plt.legend(handles=legend_patches)

ctx.add_basemap(ax=ax, crs=2193)

/data/miniconda3/envs/house-upzone/lib/python3.8/site-packages/geopandas/geodataframe.py:1322: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super(GeoDataFrame, self).__setitem__(key, value)

f. Hdist_SH Minimum Haversine distance to Residential - Single House Zone

g. Hdist_MHS Minimum Haversine distance to Residential - Mixed Housing Suburban Zone

h. Hdist_MHU Minimum Haversine distance to Residential - Mixed Housing Urban Zone

i. Zone Hdist_THA Minimum Haversine distance to Residential - Terrace Housing and Apartments

Note: this is the real name for i: 'Residential - Terrace Housing and Apartment Building Zone'

%%time
postfix2name = {
            'SH': 'Residential - Single House Zone',
            'MHS': 'Residential - Mixed Housing Suburban Zone',
            'MHU': 'Residential - Mixed Housing Urban Zone',
            'THA': 'Residential - Terrace Housing and Apartment Building Zone'
               }
for postfix, zone in tqdm(postfix2name.items()):
    resid_gdf = resid[resid.ZONE_resol == zone].dissolve()
    parcels_sample[f'Hdist_{postfix}'] = parcels_sample.apply(lambda x: x.geometry.distance(resid_gdf.geometry[0]), axis=1)

CPU times: user 1min 42s, sys: 78.1 ms, total: 1min 42s
Wall time: 1min 42s

postfix = 'THA'
column = f'Hdist_{postfix}'
subsample = parcels_sample.sample(10)
subsample['buffer'] = subsample.apply(lambda x: x.geometry.buffer(x[column]), axis=1)
subsample['geometry'] = subsample['buffer']
ax = subsample.plot(color='red', alpha=0.4)
resid[resid.ZONE_resol == postfix2name[postfix]].plot(ax=ax)
ctx.add_basemap(ax, crs=2193)

3. Local Area (LA) Information (information on the LA that the consent is located in):

a. Name (just the string is fine; I did not generate numeric codes for the local areas generated) Local_Area_name

LA = gpd.read_file('input/Modified_Community_Boards_SHP.zip').to_crs(2193)
LA.sample(3)

	OBJECTID	Local_Area	geometry
8	10.0	Hibiscus Coast	MULTIPOLYGON (((1752023.352 5954803.281, 17520...
17	21.0	Papatoetoe	POLYGON ((1765105.421 5908611.893, 1765113.661...
2	25.0	Rodney-Kumeu-Riverhead	POLYGON ((1742532.808 5931237.574, 1742490.377...

parcels_sample['geometry'] = parcels_sample['geometry_centroid_2193']
parcels_sample = gpd.sjoin(parcels_sample, LA[['Local_Area', 'geometry']]).drop(columns=['index_right'])
parcels_sample = parcels_sample.rename(columns={'Local_Area': 'Local_Area_name'})

4. 2018 Statistical Area 2 Information (information on the 2018SA2 that the consent is located in)

a. Name (string) SA22018_name

b. Numeric code SA22018_code

sa2 = gpd.read_file('NZ-SA/statistical-area-2-2020-generalised.gdb').to_crs(2193)
sa2.sample(3)

%%time
parcels_sample['geometry'] = parcels_sample['geometry_centroid_2193']
parcels_sample = gpd.sjoin(parcels_sample, sa2[['SA22020_V1_00_NAME', 'SA22020_V1_00', 'geometry']]).drop(columns=['index_right'])
parcels_sample = parcels_sample.rename(columns={'SA22020_V1_00_NAME': 'SA22018_name', 'SA22020_V1_00': 'SA22018_code'})

CPU times: user 8.29 s, sys: 3.88 ms, total: 8.29 s
Wall time: 8.29 s

5. Area Unit Information (information on the 2013AU  that the consent is located in):

a. Name (string) AU2013_name

b. Numeric code AU2013_code

au2013 = gpd.read_file('input/area-unit-2013.gdb.zip').to_crs(2193)
au2013.sample(3)

	AU2013_V1_00	AU2013_V1_00_NAME	AREA_SQ_KM	LAND_AREA_SQ_KM	Shape_Length	geometry
1481	573903	Newlands North	0.804087	0.804087	5958.640337	MULTIPOLYGON (((174.82896 -41.21949, 174.83011...
958	525202	Kawakawa-Orere	105.216001	105.216001	57437.556145	MULTIPOLYGON (((175.14259 -36.93214, 175.14265...
608	597102	Inland Water-Lake Ellesmere South	63.304671	0.000000	75233.330853	MULTIPOLYGON (((172.57398 -43.76779, 172.57401...

%%time
parcels_sample['geometry'] = parcels_sample['geometry_centroid_2193']
parcels_sample = gpd.sjoin(parcels_sample, au2013[['AU2013_V1_00_NAME', 'AU2013_V1_00', 'geometry']]).drop(columns=['index_right'])
parcels_sample = parcels_sample.rename(columns={'AU2013_V1_00_NAME': 'AU2013_name', 'AU2013_V1_00': 'AU2013_code'})

CPU times: user 6.96 s, sys: 0 ns, total: 6.96 s
Wall time: 6.96 s

6. 2018 Meshblock Information (information on the 2018MB  that the consent is located in):

a. Code MB2018_code

mb2018 = gpd.read_file('input/meshblock-2018-clipped-generalised.gdb.zip').to_crs(4326)
mb2018.sample(3)

	MB2018_V1_00	LANDWATER	LANDWATER_NAME	LAND_AREA_SQ_KM	AREA_SQ_KM	SHAPE_Length	geometry
52865	4011926	12	Mainland	0.173710	0.173710	2129.422679	MULTIPOLYGON (((174.91945 -36.94519, 174.92053...
23832	1429800	12	Mainland	0.021918	0.021918	731.394363	MULTIPOLYGON (((176.91682 -39.49291, 176.91702...
15554	0759520	12	Mainland	0.020695	0.020695	928.355735	MULTIPOLYGON (((174.91840 -37.01411, 174.91871...

%%time
parcels_sample['geometry'] = parcels_sample['geometry_centroid']
parcels_sample = gpd.sjoin(parcels_sample, mb2018[['MB2018_V1_00', 'geometry']]).drop(columns=['index_right'])
parcels_sample = parcels_sample.rename(columns={'MB2018_V1_00': 'MB2018_code'})

CPU times: user 11.3 s, sys: 8.2 ms, total: 11.3 s
Wall time: 11.3 s

7. 2013 Meshblock Information (information on the 2013MB  that the consent is located in):

a. Code MB2013_code

mb2013 = gpd.read_file('input/meshblock-2013.gdb.zip').to_crs(4326)
mb2013.sample(3)

%%time
parcels_sample['geometry'] = parcels_sample['geometry_centroid_2193']
parcels_sample = gpd.sjoin(parcels_sample, mb2013[['MeshblockNumber', 'geometry']]).drop(columns=['index_right'])
parcels_sample = parcels_sample.rename(columns={'MeshblockNumber': 'MB2013_code'})

CPU times: user 11.4 s, sys: 7.05 ms, total: 11.4 s
Wall time: 11.4 s

8. Additional distance information from consent location

For these distance calculations, use EPSG 2193 (less distortion).

parcels_sample['geometry'] = parcels_sample['geometry_centroid']
parcels_sample = parcels_sample.to_crs(2193)
parcels_sample.sample(3)

	id	appellation	affected_surveys	parcel_intent	topology_type	statutory_actions	land_district	titles	survey_area	calc_area	geometry	geometry_centroid	geometry_polygon	SA22018_name	SA22018_code	MB2013_code	MB2018_code	AU2013_name	AU2013_code
102782	4896031	Lot 1 DP 61837	DP 61837	DCDB	Primary	None	North Auckland	NA17C/935	685.0	685.0	POINT (1773608.374 5914596.096)	POINT (174.94849 -36.89867)	MULTIPOLYGON (((174.94827 -36.89874, 174.94841...	Cockle Bay	153400	0655102	0655102	Cockle Bay	521502
119786	4931702	Lot 4 DP 166054	DP 166054	DCDB	Primary	None	North Auckland	NA100D/34	52130.0	50575.0	POINT (1783027.533 5889659.615)	POINT (175.06020 -37.12153)	MULTIPOLYGON (((175.06295 -37.12105, 175.06293...	Ararimu	166400	0815302	0815302	Hunua	521132
362342	7415715	Lot 23 DP 455616	DP 455616, DP 462513	Fee Simple Title	Primary	None	North Auckland	586776	103.0	103.0	POINT (1763989.858 5916157.717)	POINT (174.84025 -36.88632)	MULTIPOLYGON (((174.84016 -36.88629, 174.84020...	Stonefields West	144900	0465305	4000293	Stonefields	517202

a. Minimum Haversine distance to coastline Hdist_coast

There are a few different datasets that could be used for this:

- NZ Coastlines (Topo 1:50k) https://data.linz.govt.nz/layer/50258-nz-coastlines-topo-150k/
- NZ Coastline - mean high water https://data.linz.govt.nz/layer/105085-nz-coastline-mean-high-water/
- NZ Coastlines and Islands Polygons (Topo 1:50k) https://data.linz.govt.nz/layer/51153-nz-coastlines-and-islands-polygons-topo-150k/  

The first doesn't have islands (e.g. Waiheke).
The second is probably most appropriate.

%%time
bounds2193 = {'x1': 1.5e6, 'x2':2e6, 'y1':5.8e6, 'y2':6.1e6}
coastline = gpd.read_file('input/lds-nz-coastline-mean-high-water-FGDB.zip!nz-coastline-mean-high-water.gdb').to_crs(2193)
coastline = coastline.cx[bounds2193['x1']:bounds2193['x2'], bounds2193['y1']:bounds2193['y2']]
coastline_dissolved = coastline.dissolve()

%%time
parcels_sample['Hdist_coast'] = parcels_sample.apply(lambda x: x.geometry.distance(coastline_dissolved.geometry[0]), axis=1)

CPU times: user 14.3 s, sys: 0 ns, total: 14.3 s
Wall time: 14.3 s

# if distance work, then red circles should extend to the nearest coastline, and no further
subsample = parcels_sample.sample(10)
subsample['coast_buffer'] = subsample.apply(lambda x: x.geometry.buffer(x.Hdist_coast), axis=1)
subsample['geometry'] = subsample['coast_buffer']
ax = subsample.plot(color='red', alpha=0.4)
coastline.cx[1.7e6:1.8e6, 5.85e6:5.97e6].plot(ax=ax)

<AxesSubplot:>

b. Minimum Haversinedistance to Open Space(set of AUP Zones) Hdist_open

c. Minimum Haversine distance to motorway Hdist_motorway

d. Minimum Haversine distance to main arterial road Hdist_main_road

roads = gpd.read_file('input/lds-nz-road-centrelines-topo-150k-FGDB.zip!nz-road-centrelines-topo-150k.gdb').to_crs(2193)
roads = roads.cx[bounds2193['x1']:bounds2193['x2'], bounds2193['y1']:bounds2193['y2']]
highways = roads[~roads.hway_num.isna()]
highways_dissolved = highways.dissolve()

ax = highways.plot()
ctx.add_basemap(ax, crs=2193)

parcels_sample['geometry'] = parcels_sample['geometry_centroid_2193']
parcels_sample['Hdist_motorway'] = parcels_sample.apply(lambda x: x.geometry.distance(highways_dissolved.geometry[0]), axis=1)

# if distance work, then red circles should extend to the nearest coastline, and no further
subsample = parcels_sample.sample(10)
subsample['highway_buffer'] = subsample.apply(lambda x: x.geometry.buffer(x.Hdist_motorway), axis=1)
subsample['geometry'] = subsample['highway_buffer']
ax = subsample.plot(color='red', alpha=0.4)
highways.plot(ax=ax)

<AxesSubplot:>

e. Minimum Haversine distance to rail line Hdist_rail

railroads = gpd.read_file('input/lds-nz-railway-centrelines-topo-150k-SHP.zip').to_crs(2193)
railroads = railroads.cx[bounds2193['x1']:bounds2193['x2'], bounds2193['y1']:bounds2193['y2']]
railroads_dissolved = railroads.dissolve()

parcels_sample['geometry'] = parcels_sample['geometry_centroid_2193']
parcels_sample['Hdist_rail'] = parcels_sample.apply(lambda x: x.geometry.distance(railroads_dissolved.geometry[0]), axis=1)

# if distance work, then red circles should extend to the nearest coastline, and no further
subsample = parcels_sample.sample(10)
subsample['rail_buffer'] = subsample.apply(lambda x: x.geometry.buffer(x.Hdist_rail), axis=1)
subsample['geometry'] = subsample['rail_buffer']
ax = subsample.plot(color='red', alpha=0.4)
railroads_dissolved.plot(ax=ax)
ctx.add_basemap(ax, crs=2193)

f. Haversine distance to downtown (use Skytower coordinates) Hdist_skytower

skytower = [-36.84838748948485, 174.7621736911587]
skytower = gpd.points_from_xy(x=[skytower[1]], y=[skytower[0]])
skytower = gpd.GeoDataFrame([{"name": "Skytower", "value": 1}], geometry=skytower, crs="EPSG:4326").to_crs(epsg=2193)

parcels_sample['geometry'] = parcels_sample['geometry_centroid_2193']
parcels_sample['Hdist_skytower'] = parcels_sample.apply(lambda x: x.geometry.distance(skytower.geometry[0]), axis=1)

# if distance works, then red circles should extend to the nearest sky tower, and no further
subsample = parcels_sample.sample(10)
subsample['skytower_buffer'] = subsample.apply(lambda x: x.geometry.buffer(x.Hdist_skytower), axis=1)
subsample['geometry'] = subsample['skytower_buffer']
ax = subsample.plot(color='red', alpha=0.2)
skytower.plot(ax=ax, color='black')
coastline.cx[1.7e6:1.8e6, 5.85e6:5.97e6].plot(ax=ax)

<AxesSubplot:>

9. Special Housing Area (SpHAs)

Indicator (1 or 0) for consent located in SpHAs SpHA_indicator
Note: here I've used centroids. Maybe should use parcel polygons instead.

spha = gpd.read_file('input/AC_Special_Housing_Area.zip').to_crs(2193)
spha_dissolved = spha.dissolve()
assert(len(spha_dissolved) == 1)

%%time
parcels_sample['geometry'] = parcels_sample['geometry_centroid_2193']
parcels_sample['SpHA_indicator'] = parcels_sample.apply(lambda x: spha_dissolved.geometry.contains(x.geometry), axis=1)

# if distance works, then red circles should extend to the nearest sky tower, and no further
subsample = parcels_sample.sample(500)
ax=subsample.plot(column='SpHA_indicator', alpha=0.6)
plt.ylim((5.89e6, 5.95e6))
plt.xlim((1.73e6, 1.78e6))
spha_dissolved.boundary.plot(ax=ax)
ctx.add_basemap(ax, crs=spha_dissolved.crs)

10. Ranged Address Indicator

11. Matching Type Indicators

a. LINZ_MATCH_CODE

b. LINZ_2ND_MATCH_CODE